1 Rural labour allocation and urbanisation in Sub-Saharan Africa VERY INCOMPLETE DRAFT, PLEASE DO NOT CITE Hanne Van Cappellen Institute of Development Policy (IOB), University of Antwerp, Belgium [email protected]– P +32 3 265 54 22 Joachim De Weerdt Institute of Development Policy (IOB), University of Antwerp, Belgium LICOS, Catholic University of Leuven, Belgium [email protected]– P +32 3 265 57 69 Abstract Over the last few decades, Sub-Saharan Africa has been urbanizing at an unprecedented rate. While there is evidence that this has led to rural-to-urban migration, real structural transformation has not taken place: the majority of Africa’s poor people still live in rural areas and are primarily engaged in low productivity agriculture. This paper addresses the link between urbanisation and the rural labour market. It is hypothesised that urbanisation stimulates both the demand for and supply of more working hours outside of agriculture. The proximity of an urban agglomeration induces a demand for diversified employment, and the highly seasonal agricultural calendar offers space for off-farm employment. By combining panel data on employment and night light data as a proxy for urbanisation, this paper explores the spatial and temporal link between rural labour supply and the proximity of agglomerations in three sub-Saharan African countries for the period 2008-2016. Not only does it evaluates the effect of urbanisation on the number of hours supplied, but it also provides insight in how these hours are allocated sector wise. Using LSMS-ISA employment data on a panel of more than 15 000 individuals, allows us to shift the focus from sector productivity to individual level productivity, as well as account for individual fixed effects. Nightlight data have shown to be a good proxy for urbanisation and are particularly interesting for Sub-Saharan Africa, where urbanisation statistics lag behind reality, are only sporadically available and lack international comparability. They provide us with fine-grained urbanisation information with which we can investigate the role of the emerging small towns (which are mushrooming up all over Africa) on the rural population. Our analysis finds that urbanisation has a significant positive effect in rural areas on hours supplied in wage labour as well as on the share of households engaging in non-farm enterprises. The fact that hours worked are not affected negatively confirms the premise that the seasonal agricultural schedule offers space for the supply of more working hours. These findings have the potential to inform the formulation of labour policies as well as urban planning that can maximize the positive effects of African urbanisation on the rural poor.
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1
Rural labour allocation and
urbanisation in Sub-Saharan Africa
VERY INCOMPLETE DRAFT, PLEASE DO NOT CITE
Hanne Van Cappellen Institute of Development Policy (IOB), University of Antwerp, Belgium
Table 6 reports the fixed effects estimation with Conley Standard Errors for the baseline
specification. It looks at the effect of urbanisation in terms of SOL (expressed in SD) in different
concentric circles around the EA on both hours supplied to wage (and other non-farm) labour,
labour on own farm and the share of households engaged in non-farm enterprises. Specification
(1) shows that a one standard deviation increase in the sum of light in the 0-5 km concentric circle
around the EA translates into an increase in hours worked in wage labour of 139.8 hours a year.
Similarly, this increases the probability of a household to have any non-farm enterprise with 7.1
percentage points. Given the mean baseline values of hours worked in wage labour of 219.08
hours and the percentage of households engaging in non-farm enterprises of 34.50%, this effect is
quite substantial. However, it is not straightforward to interpret a one SD increase in SOL. So to
place this effect in a more relatable setting, it is informative to look at the relationship between the
size of an agglomeration and its SOL value.
In our sample, the average SOL value of an agglomeration of an area size between 40-80 km² is
330. An agglomeration of a size between 100 – 150 km² has an average SOL value of 927. This
implies that the growth of a small agglomeration into a medium agglomeration means an average
increase in around 600 SOL. If this happens in the 0-5km concentric circle, the hours worked in
wage (and other non-farm) labour increase with 61 hours a year. Similarly will the probability of
a household to have a non-farm enterprise increase with 3 percentage points. For the 5-10
concentric circle, this effect would be an increase of 34 hours and 4.5 percentage points
respectively.
Table 6 also shows that the effect of urban growth on labour allocation outside of the farm dies
out quickly: after a distance of 10 kilometres radius around the EA, no significant effect on wage
(and other non-farm) labour and on non-farm enterprises is observed anymore. This indicates that
it is especially nearby urbanisation that can positively affect hours supplied of rural individuals.
Specification (3) of Table 6 estimates the effect of urbanisation on hours worked on the household
farm, and finds a positive effect on hours worked in the household farm in four of the six concentric
circles. This provides a first indication that urbanisation is not only providing opportunities to fill
in gaps in the labour calendar with off-farm activities, but that it is also positively affecting the
agricultural sector.
20
Table 6.
(1) (2) (3)
Wage and other
(non-farm) labour
% of hh with non-
farm enterprise
(own) farm
labour
Baseline average 219.08 34.50 272.92
1 year lagged sol 0-5 km in SD 139.8*** 0.0714* 2.472 (1372.66) (33.09) (0.0409) (23.21)
1 year lagged sol 5-10 km in SD 103.6*** 0.137*** 129.0** (1811.75) (35.78) (0.0411) (64.88)
1 year lagged sol 10-20 km in SD 41.17 -0.0402 107.3** (2711.19) (47.85) (0.0414) (51.59)
1 year lagged sol 20-30 km in SD 39.84 -0.0383 92.38** (2150.103) (41.97) (0.0331) (39.83)
1 year lagged sol 30-40 km in SD 10.95 0.00890* -6.956 (3060.24) (6.702) (0.00519) (6.709)
1 year lagged sol 40-50 km in SD -2.439 -0.000144 28.49*** (2527.25) (2.102) (0.00291) (3.575)
Observations 26,975 35,954 36,119
R-squared 0.001 0.002 0.004
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
6.2 Agglomeration effects
The baseline specification estimated the effect on an increase of urbanisation in terms of an
increase of sum of lights, regardless of whether this increase came from growth in one particular
city, or from growth divided among different (smaller) cities. However, it is plausible these two
cases have a different impact on labour allocations due to agglomeration effects. (tbc – Table 7)
21
Table 7.
(1) (2) (3)
Wage and other
(non-farm) labour
% of hh with non-
farm enterprise
(own) farm labour
Baseline average 219.08 34.50 272.92
1 year lagged sol 0-5 km in SD 319.8*** 0.357** 15.25 (1372.66) (103.8) (0.154) (150.4)
1 year lagged sol 5-10 km in SD -2,055*** -1.559* -711.2
(566.0) (0.934) (1,601)
1 year lagged sol 5-10 km in SD -652.3** 2.733*** -7,641***
(265.4) (0.316) (609.0)
Aggl 0-5 km * sol 0-5 km (1Y lag) -0.138* -0.000226** 0.00171
(0.0726) (0.000115) (0.109)
1 year lagged # aggl 5-10 km 44.36 0.128*** -145.5***
(28.78) (0.0424) (42.64)
Aggl 5-10 km * sol 5-10 km (1Y lag) 1.181*** 0.000941* 0.457
(0.311) (0.000514) (0.881)
1 year lagged # aggl 5-10 km 19.55 0.0350 -79.39
(18.62) (0.0314) (55.90)
Aggl 10-20 km * sol 10-20 km (1Y lag) 0.240** -0.00101*** 2.819***
(0.0980) (0.000116) (0.224)
1 year lagged # aggl 10-20 km -12.45*** -0.0477*** -24.65**
(4.364) (0.00584) (11.34)
Observations 26,975 35,954 36,119
R-squared 0.001 0.003 0.002
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
6.3 The effect of differential in baseline surrounding urbanisation level
Tbc
6.4 Socio-economic differential effects
Interactions with sex, age : tbc
22
7. Robustness checks
In this section we will show that our findings are robust to alternative specifications.
As such we will control for existing road network in rural areas.
(tbc)
8. Conclusion
So far, true structural transformation in sub-Saharan Africa has not taken place. Studies
questioning the size of the agricultural productivity gap in sub-Saharan Africa cast doubts on the
potential gains to be materialised by labour moving out of agriculture.
McCullough (2017) showed that agricultural labourers have an excess of labour hours to be
absorbed by a demand inside and/or outside of agriculture. Our findings resonate with this premise
as they provide evidence that (nearby) urbanisation offers opportunities in rural areas to fill
employment gaps in the agricultural labour schedule by supplying working hours outside of
agriculture. As a consequence, (nearby) urbanisation might positively affect rural poverty. This is
also in line with the findings of Nagler and Naudé (2014) that a significant part of non-farm
entrepreneurship in sub-Saharan Africa serves to complement seasonal agricultural labour. Further
does it add to papers that find evidence for growth linkages between the agricultural and non-
agricultural sector, as well as the finding from Calì and Menon (2013) that urbanisation has a
poverty reducing effect largely due to spill overs from the urban economy, and not necessarily due
to rural-urban migration.
Secondly, did this paper find evidence that it is especially nearby urbanisation that is affecting
rural labour supply choices. This adds to the continuously expanding literature that shows that
growth in small towns can provide an important alley into rural poverty reduction (Christiaensen,
2013; Christiaensen et al., 2017; Christiaensen & Kanbur, 2017; Gibson et al., 2017). This has also
potential implications for policymaking; investing in thriving small towns is not only beneficial
for the urban population, but also has positive spill-overs to the surrounding rural areas.
A further understanding of the tight connection between urbanisation and labour patterns is
essential for designing policies that may stimulate both agricultural and off-farm economic
activities. Until now, rural entrepreneurship has been largely neglected in policy strategies for rural
development (Nagler & Naudé, 2013). As Africa’s poor still mainly live in rural areas,
understanding the livelihood strategies of the rural population is key for informing policies that
can strengthen the possibilities of the rural population for fuller employment, and thus initiate rural
poverty reduction.
23
Appendix
Table A
Non-farm enterprises survey questions
Ethiopia Malawi
Over the past 12 months, has anyone in this
household ..
1) … owned a non-agricultural business or
provided a non-agricultural service from
home or a household-owned shop, as a
carwash owner, metal worker, mechanic,
carpenter, tailor, barber, etc.?
2) … processed and sold any agricultural by-
products, including flour, local beer
(tella), 'areke", "enjera", seed, etc., but
excluding livestock by-products,
fresh/processed fish?
3) … owned a trading business on a street or
in a market?
4) … offered any service or sold anything on
a street or in a market, including firewood,
home-made charcoal, construction timber,
woodpoles, traditional medicine, mats,
bricks, cane furniture, weave baskets,
thatch grass etc.?
5) … owned a professional office or offered
professional services from home as a
doctor, accountant, lawyer, translator,
private tutor, midwife, mason, etc?
6) … driven a household-owned taxi or pick-
up truck to provide transportation or
moving services?
7) … owned a bar or restaurant?
8) … owned any other non-agricultural
business, even if it is a small business run
from home or on a street?
Over the past 12 months has anyone in your
household…
1) … owned a non-agricultural business or
provided a non-agricultural service from
home or a household-owned shop, as a
carwash owner, metal worker, mechanic,
carpenter, tailor, barber, etc.?
2) … processed and sold any agricultural by-
products, including flour, starch, juice,
beer, jam, oil, seed, bran, etc., but
excluding livestock by-products,
fresh/processed fish?
3) … owned a trading business on a street or
in a market?
4) … offered any service or sold anything on
a street or in a market, including firewood,
home-made charcoal, curios, construction
timber, woodpoles, traditional medicine,
mats, bricks, cane furniture, weave
baskets, thatch grass etc.?
5) … owned a professional office or offered
professional services from home as a
doctor, accountant, lawyer, translator,
private tutor, midwife, mason, etc?
6) … driven a household-owned taxi or pick-
up truck to provide transportation or
moving services?
7) … owned a bar or restaurant?
8) …owned any other non-agricultural
business, even if it is a small business run
from home or on a street?
24
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